绘制具有相同X轴和多个Y轴且数据数量未定义的图形

Question

我开发了一个软件，可以从csv文件中读取数据，并在多个Y轴的图表中显示数据。对于csv文件中的每个信号，我在图表中都有一行，图表中的每一行都有一个轴。

如果csv文件中包含的信号数量未知，因此例如有六个以上的信号(S1…S7、S8、S9等(，如何为每个信号自动生成Y轴？？

现在我的程序已经构建好了，就像我知道csv文件中的信号数量一样。我已经尝试创建一个for来修改关键字yaxis，但这是不可能的。

我的csv文件：

DateTime;S1;S2;S3;S4;S5;S6
2020-07-17 09:57:27.119916;725.9926027110598;730.5730869210306;946.2937510737263;542.341137182406;758.5531610786929;512.2027881299339
2020-07-17 09:57:28.119916;761.1846087077208;984.1009029835216;974.8724733720549;576.8019892357476;751.6553704523698;855.5439493088621
2020-07-17 09:57:29.119916;618.7837289058051;823.9970681226491;594.2841714340789;873.3093170922189;770.0875733375253;681.1715820388949
2020-07-17 09:57:30.119916;515.9456035777555;533.017970929369;639.3409213385498;542.4405737836958;514.4985515824058;650.5229638670448
2020-07-17 09:57:31.119916;589.1350057317254;605.703259361724;602.3181712775759;860.9749699475683;801.7960812507487;562.400896160191
2020-07-17 09:57:32.119916;626.2528314431347;615.7078057434281;643.2023497200336;709.6997180536518;741.365852401098;712.4384053449293
2020-07-17 09:57:33.119916;553.9768845577024;961.7714859567449;519.8207498752649;551.8006708566627;511.7426656331682;849.3428394570542
2020-07-17 09:57:34.119916;994.8208541190293;700.59423301376;569.1853469890981;997.5842090634065;621.2070112896865;848.5079857917269
2020-07-17 09:57:35.119916;502.2301607876932;760.8787524302393;671.2907579865052;669.0718770518221;901.3788876259023;926.077760311429
2020-07-17 09:57:36.119916;578.3978109170034;811.407262562966;822.6244615030105;570.0016494663124;935.0853062150045;689.8800124555897

我的代码：

df = pd.read_csv(file_name, delimiter = ';')

fig = go.Figure()
for i in range(1, len(df.columns)):
fig.add_trace(go.Scatter(x=df.iloc[:,0], y=df.iloc[:,i], name=df.columns[i], yaxis='y'+str(i)))

fig.update_layout(
xaxis=dict(
domain=[0.3, 0.7]
),
yaxis=dict(
title="S1",
titlefont=dict(
color="#1f77b4"
),
tickfont=dict(
color="#1f77b4"
),
side="left",
position=0.10        
),
yaxis2=dict(
title="S2",
titlefont=dict(
color="#ff7f0e"
),
tickfont=dict(
color="#ff7f0e"
),
anchor="free",
overlaying="y",
side="left",
position=0.20
),
yaxis3=dict(
title="S3",
titlefont=dict(
color="#d62728"
),
tickfont=dict(
color="#d62728"
),
anchor="x",
overlaying="y",
side="left",
position=0.30
),
yaxis4=dict(
title="S4",
titlefont=dict(
color="#6427bd"
),
tickfont=dict(
color="#6427bd"
),
anchor="free",
overlaying="y",
side="right",
position=0.70
),
yaxis5=dict(
title="S5",
titlefont=dict(
color="#3758bd"
),
tickfont=dict(
color="#3758bd"
),
anchor="free",
overlaying="y",
side="right",
position=0.80
),
yaxis6=dict(
title="S6",
titlefont=dict(
color="#2690bd"
),
tickfont=dict(
color="#2690bd"
),
anchor="free",
overlaying="y",
side="right",
position=0.90
)
)

fig.update_layout(
title_text="Stacked y-axes",
yaxis={'title':'Values [unit]'}, 
xaxis={'title':'DateTime'},
height=600, 
width=1000
)

plot(fig)

这是我现在的结果：https://drive.google.com/file/d/1QPLG0gT2SvWd4_0-piGQsdRy_eTACFn1/view?usp=sharing

Answer 1

您可以在for循环中构建所有yaxis*参数。在下面的示例中，我创建了一个名为axes的dict，添加每个yaxis*子dict，稍后我调用fig.update_layouts(**axes)。

df = pd.read_csv('filename.csv', delimiter = ';')

fig = go.Figure()
colors=["#1f77b4","#ff7f0e","#d62728","#6427bd","#3758bd","#2690bd"]
axes = {
'xaxis': dict(domain=[0.3, 0.7])
}
for i in range(1, len(df.columns)):
fig.add_trace(go.Scatter(x=df.iloc[:,0], y=df.iloc[:,i], name=df.columns[i], yaxis='y'+str(i)))
s = 'yaxis'
if i > 1:
s = s + str(i)
axes[s] = dict(
title=f"S{i}",
titlefont=dict(color=colors[i-1]),
tickfont=dict(color=colors[i-1])
)
if i > 1:
axes[s]['anchor'] = 'free'
axes[s]['overlaying'] = 'y'
if i > len(df.columns) / 2:
axes[s]['side'] = 'right'
axes[s]['position'] = 0.3 + 0.1*i
else:
axes[s]['side'] = 'left'
axes[s]['position'] = 0.1*i
fig.update_layout(**axes)
fig.update_layout(
title_text="Stacked y-axes",
yaxis={'title':'Values [unit]'}, 
xaxis={'title':'DateTime'},
height=600, 
width=1000
)